Role of inflammatory biomarkers in mediating the effect of lipids on spontaneous intracerebral hemorrhage: a two-step, two-sample Mendelian randomization study

Background Spontaneous intracerebral hemorrhage (sICH) is a form of stroke with high mortality rates and significant neurological implications for patients. Abnormalities in lipid metabolism have been implicated in various cardiovascular diseases, yet their relationship with sICH remains insufficiently explored, particularly concerning their association with inflammatory factors. Methods Employing a two-sample, two-step Mendelian Randomization approach, combined with data from GWAS datasets, to investigate the causal relationship between plasma lipid levels and sICH. Additionally, the role of inflammatory factors in this relationship was examined, and sensitivity analyses were conducted to ensure the robustness of the results. Results The results indicate a significant causal relationship between 19 plasma lipid metabolites and sICH. Furthermore, mediation analysis revealed that three distinct lipids, namely Sterol ester (27:1/20:2), Phosphatidylcholine (16:0_20:4), and Sphingomyelin (d34:1), exert their influence on sICH through inflammatory factors. TRAIL (OR: 1.078, 95% CI: 1.016–1.144, p = 0.013) and HGF (OR: 1.131, 95% CI: 1.001–1.279, p = 0.049) were identified as significant mediators. Conclusion This study provides new evidence linking abnormalities in lipid metabolism with sICH and elucidates the role of inflammatory factors as mediators. These findings contribute to a better understanding of the pathogenesis of sICH and offer novel insights and therapeutic strategies for its prevention and treatment.


Introduction
Spontaneous intracerebral hemorrhage (sICH), resulting from the rupture of blood vessels in the brain, accounts for 10-15% of total stroke occurrences and represents a significant contributor to neurological morbidity and mortality, with a 30-day mortality rate ranging from 35 to 52%.Half of these fatalities occur within the initial 48 h, imposing a significant burden on families and society (1,2).The incidence of sICH increases with age, particularly among individuals aged 65 and above.However, it can also occur in younger populations, especially in the presence of risk factors.Hypertension is one of the primary risk factors for sICH.Other risk factors include smoking, alcohol consumption, vascular diseases, diabetes, and hyperlipidemia (3)(4)(5).
Lipids constitute a fundamental category of organic compounds present in living organisms, encompassing fats, fatty acids, triglycerides, phospholipids, cholesterol, and other constituents.Lipids are indispensable for various cellular functions, including cell membrane integrity, energy storage and release, signaling cascades, and hormone synthesis (6,7).Dyslipidemia, characterized by aberrant lipid metabolism, is intricately linked to the development and progression of numerous ailments, such as cardiovascular diseases, obesity, fatty liver disease, and metabolic syndrome (8,9).In terms of the impact of dyslipidemia on cerebrovascular ailments, prior investigations have established that conditions like hypercholesterolemia and atherosclerosis can result in cerebral blood vessel constriction or obstruction, with hyperlipidemia emerging as a recognized risk factor for ischemic stroke (10).While previous studies have primarily focused on the relationship between lipids and ischemic stroke, emerging evidence suggests that dyslipidemia may also be associated with the risk of sICH (11)(12)(13).When lipid metabolism becomes abnormal, a series of complex biological changes may occur in the body, directly or indirectly increasing the risk of hemorrhagic stroke.Firstly, abnormal lipid metabolism may recruit immune cells and lead to the release of pro-inflammatory factors.These inflammatory responses can result in endothelial cell damage, leading to structural changes in the blood vessel walls (14,15).Endothelial damage may constitute a fundamental basis for increased vessel susceptibility to rupture (15).Secondly, abnormal lipid metabolism may accelerate the formation and development of atherosclerotic plaques.The instability of these plaques increases the risk of rupture, particularly in complex plaques where macrophage-driven inflammatory responses and macrophage apoptosis may lead to the release of lipid contents, thereby further promoting plaque rupture (14,16).Therefore, abnormal lipid metabolism may lead to structural changes in the blood vessel walls, reduced elasticity, and increased brittleness, thereby making the vessels more prone to rupture and bleeding.Previous studies have found that low total cholesterol levels may promote cell necrosis in the arterial intima, rendering it susceptible to microaneurysms and associated with the onset of hemorrhagic stroke (17).
Moreover, certain lipid substances, such as low-density lipoprotein cholesterol, may promote platelet activation and tissue factor expression, leading to impaired coagulation function, which may play a role in the pathogenesis of cerebral hemorrhage (18).These mechanisms may interact with each other, collectively increasing the risk of hemorrhagic stroke occurrence.In conclusion, the etiology of sICH is multifactorial, with inflammation potentially augmenting vascular permeability and exacerbating vascular pathologies such as arteriosclerosis and aneurysm formation, thereby increasing the risk of vascular rupture and subsequent intracranial bleeding (14).This finding has spurred further investigation into the role of lipid substances in the pathophysiology of stroke, offering new perspectives for the development of novel therapeutic strategies.Therefore, exploring the relationship between lipid substances and sICH holds significant importance.
Mendelian randomization (MR) is an analytical approach that leverages genetic variation to mimic randomized controlled trials (RCTs), facilitating causal inference regarding the relationship between risk factors and diseases (19).It serves to mitigate the influence of confounding variables and address issues of reverse causation.It has been extensively employed to investigate causal associations between exposures and diseases (19,20).Based on the aforementioned conditions and background, a two-sample MR analysis will be conducted to elucidate the relationship between lipid levels and sICH.Subsequently, a mediation MR analysis will be performed to assess the potential role of inflammatory factors within this association, thereby exploring the underlying mechanistic pathways involved.

Data resources for plasma lipidome, inflammatory biomarkers, and sICH
Data of plasma lipidome were obtained from a univariate and multivariate GWAS involving 179 lipid species across 13 lipid classes from 7,174 Finnish individuals in the GeneRISK cohort (21).This study performed a phenome-wide association study on identified lipid-related genetic loci among 377,277 participants from the FinnGen biobank, followed by colocalization analysis of these endpoints (accession numbers GCST90277238 to GCST90277287).
The inflammatory biomarkers data were obtained from Zhao et al. 's study (22).In this research, a thorough evaluation of genetic influences on inflammation-related proteins was conducted using a genome-wide analysis of protein quantitative trait loci.The study encompassed 14,824 participants of European descent and involved the measurement of 91 plasma proteins via the Olink panel.Accession numbers GCST90274758 to GCST90274848.
The data of sICH were acquired from the FinnGen dataset consisting of 7,040 cases and 374,631 controls of European ancestry. 1 The FinnGen project is an open large-scale genetic research initiative originating from Finland, utilizing samples from extensive populations across various regions within the country.Its objective is to employ genetic research methodologies, particularly genome-wide association studies (GWAS), to elucidate the associations between genetic variations and diseases as well as health characteristics.The FinnGen dataset comprises substantial genomic, clinical, and biosample information, which can be utilized for investigating a multitude of

Selection of genetic instrumental variables
According to previous researches (24,25), we employed a threshold of p < 1 × 10^-5 to select SNPs associated with the plasma lipidome.In the reverse MR analysis, SNPs associated with sICH were selected using the same criteria.Subsequently, we performed a clumping procedure to ensure independence among the chosen SNPs (r^2 < 0.001, clumping window = 10,000 kb) (26).To assess the reliability of each SNP, we computed the F-statistic and retained only those with values exceeding 10 (27).This stringent criterion ensured the robustness of our instrumental variables.The F-statistic for each SNP was calculated using the formula F = R^2 / (1 − R^2) × (N-2), where R^2 represents the variance of exposure explained by the instrumental variables (IVs), and N denotes the sample size.Furthermore, we calculated the variance of exposure explained by the instrument variable using the formula R^2 = β^2 / (β^2 + se^2 × N), where β denotes the effect size for the genetic variant of interest, she represents the standard error for β, and N indicates the sample size.

Statistical analysis
We utilized the Inverse Variance Weighting (IVW) method as our primary approach, which effectively leverages multiple single nucleotide polymorphisms (SNPs) as instrumental variables to estimate the causal impact of the exposure on the outcome.This method aggregates effect estimates of individual SNPs through inverse variance weighting, aiming to maximize the precision of the combined effect and thereby yield robust (28).Additionally, we employed weighted mode, MR-Egger, Simple mode, and weighted median tests to assess the effects, ensuring accuracy and robustness by detecting causal relationships from various aspects.
We employed a two-sample MR approach to dissect the direct and indirect effects of the plasma lipidome on sICH.In addition to the fundamental effect estimate (β1) of the plasma lipidome on inflammatory factors obtained from univariable MR analysis, two additional estimates were calculated: the causal effect of the mediator (inflammatory biomarkers) on sICH (β2), and the total effect of the plasma lipidome on sICH (β0).The mediation effect refers to the causal impact of the plasma lipidome on sICH through the mediator (inflammatory biomarkers), which can be estimated using the coefficient product method (β1 × β2).Hence, the proportion of the indirect effect can be calculated as "mediation effect/total effect" ([β1 × β2]/β0) (Figure 1).

Sensitivity analysis
Sensitivity analysis involves assessing the robustness of causal estimates to potential biases and confounding factors.We utilized Cochran's Q test to evaluate the heterogeneity in the impact of genetic variants on the exposure factor.Additionally, MR-Egger intercept and MR-PRESSO analyses were conducted to assess the potential influence of pleiotropy on the outcomes of the MR analysis.Simultaneous utilization of scatter plots and funnel plots for data visualization and quality assurance aided in a comprehensive examination of the accuracy and reliability of the analytical results.Finally, a leave-one-out sensitivity analysis was conducted, removing one SNP at a time, to investigate if any single SNP was responsible for the causal association.
The effect estimates were reported as odds ratios (ORs) with their 95% confidence intervals (CIs).Statistical significance was defined as a two-sided p-value <0.05.All analyses were conducted using the "TwoSampleMR" package (version 0.5.8) in the R software (version 4.3.1).

Mediation effect of inflammatory biomarkers
TRAIL and HGF were significantly associated with both specific plasma lipidome and sICH.A mediation effect of plasma lipidome on sICH via TRAIL and HGF was observed.We found that the highest proportion was for the effect of plasma Phosphatidylcholine (16:0_20:4) concentration mediated by TRAIL on sICH, with a mediation effect of 13.2%, while the lowest was for the effect of plasma Sterol ester (27:1/20:2) concentration mediated by TRAIL on sICH, which was only 4.2%.The mediation effects of different mediators are demonstrated in Table 5.All the results of two-step MR analysis were demonstrated in Figure 2.

Sensitivity analysis
After rigorous screening, the number of eligible SNPs serving as IVs in the plasma lipidome for sICH at exposure are as follows: 27, 24, and 32, corresponding to Sterol ester (27:1/20:2), Phosphatidylcholine (16:0_20:4), and Sphingomyelin (d34:1).In the reverse MR analysis, based on our screening criteria, a total of 45 usable SNPs related to sICH were identified.When selecting SNPs to study the causal relationship between plasma lipidome as exposure and inflammatory factors as outcome, the determined range of available instrumental variables is from 24 to 32.Subsequently, In the process of screening for TRAIL and HGF instrumental variables, we found 36 and 30 variables, respectively.All SNPs had F-statistics ranging from 19.552 to 475.334.An F-statistic >10 is considered indicative of adequate instrument strength.
According to Cochran's Q test, there was no evidence of heterogeneity in the instrumental variables from the plasma lipidome to sICH.To assess the potential horizontal pleiotropy of SNPs, we employed MR-Egger regression, providing a valuable assessment of its presence.Sensitivity analysis results did not reveal significant evidence of directional pleiotropy (p > 0.05).During sensitivity analysis of the association between plasma lipidome and inflammatory factors, we observed no heterogeneity or horizontal pleiotropy in plasma lipidome traits.Furthermore, leave-one-out analysis demonstrated that no SNP significantly influenced the results (Figure 3), and the Scatter Plot and Funnel Plot revealed no apparent outliers or biases (Figure 4).The forest plot demonstrates that the effect estimates all point in the same direction, with no significant heterogeneity observed (Figure 5).By the research procedure, the relevant data are presented sequentially from Supplementary Tables S5-S7.

Discussion
According to the current literature, this is the first study to investigate how inflammatory factors mediate the causal pathway between plasma lipidome levels and sICH.This research discovered a possible correlation between genetically determined plasma lipidome levels and the risk of sICH.Furthermore, additional mediation analysis supported that the causal effects of plasma lipidome levels on sICH were partially mediated by inflammatory factors.
Lipids are essential for cellular function, serving as fundamental constituents of cell membranes, contributing to energy storage, maintaining equilibrium, and regulating cellular signaling pathways (7).Lipids are integral constituents of the brain, and their imbalance is correlated with disorders of the nervous system.Perturbations in lipid metabolism are connected with vascular inflammation and oxidative stress, both critical factors contributing to the development of atherosclerosis (29).We identified Sterol ester (27:1/20:2), Phosphatidylcholine (16:0_20:4), and Sphingomyelin (d34:1) as belonging to the categories of lipid esters, phospholipids, and sphingolipids, respectively.Through inflammatory mediators, they are causally associated with the onset of sICH.Sterol esters, resulting from the esterification of sterols with fatty acids, are pivotal in preserving the structural and operational integrity of cellular membranes.They modulate membrane fluidity and stability, thereby impacting cellular responsiveness to external stimuli and signal transduction.Notably, within the nervous system, sterol esters assume a critical role, particularly in the formation and sustenance of myelin sheaths.These sheaths serve as protective coatings for nerve fibers, and the presence of sterol esters aids in safeguarding nerve fibers while facilitating efficient nerve signal transmission (30).Additionally, in the context of inflammation, sterol esters may serve as signaling molecules or regulatory factors.By modulating cellular signal transduction pathways, they can affect the expression of genes related to inflammation and the release of inflammatory mediators.This modulation helps regulate the polarization state of inflammatory cells and the balance between pro-inflammatory and anti-inflammatory factors, thereby influencing the onset and progression of inflammation (31).Phosphatidylcholine constitutes a fundamental constituent within cell membranes, comprising a glycerol scaffold, dual fatty acid chains, and a phosphoric acid moiety connecting a choline unit.It serves as a critical player in upholding membrane architecture, cellular signaling cascades, choline provisioning, and transportation, alongside the pathophysiological pathways involved in disease onset (32).Among these, choline is an important nutrient involved in the synthesis of the neurotransmitter acetylcholine, maintenance of cellular membrane integrity, and metabolism of methyl (33).The metabolic pathways of choline have been associated with ischemic stroke and cognitive dysfunction after acute ischemic stroke (34,35).Sphingomyelin is a phospholipid compound found within cell membranes, exerting a crucial role in upholding membrane structure, cellular signaling, neurological system function, and metabolic modulation (36).Within the nervous system, it serves as a primary constituent of both neuronal cell membranes and myelin sheaths, essential for maintaining the structural integrity and functionality of neurons (37).Specifically, sphingomyelin contributes to the protection of neuronal cell membranes from external environmental damage while promoting the formation and maintenance of myelin sheaths, thereby ensuring efficient neural signal transmission (37).Moreover, studies suggest that Sphingomyelin acts as a signaling molecule in modulating the initiation and progression of inflammatory responses.Upon cellular inflammation stimulation, phospholipase catalyzes the hydrolysis of Sphingomyelin into phosphoric acid and choline, with phosphoric acid playing a pivotal role as a constituent of inflammatory signals.Phosphatidic acid can activate inflammatory signaling pathways, such as NF-κB and MAPK pathways, thereby promoting the production and release of inflammatory cytokines, leading to the initiation of inflammatory responses (38).In conclusion, three of them play crucial roles in cell membrane structure and function, serve as key players in the nervous system, and are involved in modulating inflammatory responses.TRAIL, categorized as a surface protein, is a member of the tumor necrosis factor (TNF) family and is predominantly expressed by activated immune cells, including T and B lymphocytes, neutrophils, dendritic cells, monocytes, macrophages, natural killer cells, and Natural Killer T cells (NKT) (39).Its main role involves maintaining the internal balance of the immune system, responding to infections, autoimmune diseases, and apoptosis.TRAIL exhibits pro-angiogenic activity and stimulates the proliferation of vascular smooth muscle cells (40)(41)(42).Conversely, TRAIL has also been found to inhibit vascular endothelial growth factor (VEGF)-mediated angiogenesis through both caspase-8-dependent and caspase-8independent mechanisms (43), thus demonstrating a dual functionality.TRAIL might impact vascular development by controlling the survival and apoptosis of endothelial cells and vascular smooth muscle cells.Recent studies have indicated that CD4+ T cells derived from atherosclerotic plaques induce apoptosis of Vascular Smooth Muscle Cells (VSMCs) through a TRAILdependent mechanism, potentially leading to plaque instability and rupture (44).Hence, apoptosis of vascular cells triggered by TRAIL may regulate cellular turnover within the vascular wall.TRAIL has the potential to trigger the activation of the NF-κB signaling pathway, resulting in heightened activation and transcriptional potency of NF-κB (45,46).NF-κB can modulate the expression of inflammationrelated genes in both endothelial cells and vascular smooth muscle cells, promoting the onset of inflammatory responses.Activation of inflammation within endothelial cells can lead to increased vascular permeability and leukocyte adhesion, thereby contributing to the development of inflammatory vascular diseases such as atherosclerosis.Additionally, during the process of angiogenesis, NF-κB influences the formation of new blood vessels by regulating the expression of angiogenesis-related factors such as VEGF and matrix metalloproteinases (MMPs) (47,48).Although research on the relationship between TRAIL and sICH is currently lacking, making it challenging to ascertain the precise mechanisms involved,    The forest plot of the Mendelian randomization analysis results.

Strengths and limitations
Our study utilized MR to infer causal relationships between exposure factors and diseases by leveraging genetic variation.Compared to observational studies, MR effectively reduces confounding factors, reverse causation, and information bias.Additionally, MR offers high statistical efficiency and minimizes reverse causation effects and information bias, making it a powerful tool for elucidating disease mechanisms.Despite these advantages, several limitations need to be addressed.MR results may be influenced by potential heterogeneity and genetic pleiotropy.Our study primarily focused on individuals of European descent, which may limit the generalizability of the findings, necessitating further investigation in diverse populations.Furthermore, although the initial threshold for exposure-related SNPs was set at p < 5×10 −8 , it was adjusted to p < 1×10 −5 due to the limited availability of effective SNPs, potentially introducing some instability in the results.Future research should encompass populations of different ethnicities and geographical regions to validate the universality and reliability of our findings.While we identified inflammatory factors as potential mediators, we did not explore other biological pathways related to sICH mediated by lipid factors.Future studies should include additional mediators, such as immune cells and oxidative stress, and validate our findings across various populations.Further research should also investigate how inflammatory factors and lipid metabolism contribute to sICH.Integrating large-scale cohort data with multi-omics approaches could provide a more comprehensive understanding of the underlying mechanisms.Exploring potential intervention methods, including pharmacological treatments, lifestyle changes, and dietary adjustments, could help reduce sICH risk and improve outcomes.

Conclusion
Through a two-step, two-sample MR analysis, this study provides robust evidence of a causal relationship between blood lipid levels and the risk of sICH.Additionally, the findings indicate that blood lipid levels may influence the risk of sICH via inflammatory pathways, thereby contributing novel insights for future strategies in prevention and monitoring.

FIGURE 1
FIGURE 1The flowchart illustrating the Mendelian randomization process.

TABLE 1
The causal effects of plasma lipidom on sICH by IVW method.

TABLE 2
The causal effects of sICH on plasma lipidom by IVW method.

TABLE 3
The causal effects of plasma lipidom on inflammatory biomarkers by IVW method.

TABLE 4
The causal effects of inflammatory biomarkers on sICH by IVW method.

TABLE 5
The mediation effect and proportion of inflammatory biomarkers by IVW method.
(52-55).Furthermore, HGF is implicated in modulating angiogenesis and neuroregeneration processes.It facilitates endothelial cell migration and proliferation, regulates vascular smooth muscle cell function, and amplifies the expression and functionality of additional angiogenic factors like VEGF, thereby expediting angiogenesis, which is vital for vascular formation and restructuring.Additionally, it fosters brain tissue restoration and reshaping by augmenting neuroregeneration mechanisms (56, 57).However, current research 10.3389/fneur.2024.1411555Frontiers in Neurology 10 frontiersin.orgkin was not required to participate in this study in accordance with the national legislation and the institutional requirements.